module ResultTests

using DiffBase
using Base.Test

using StaticArrays

using DiffBase: DiffResult, GradientResult, JacobianResult, HessianResult,
                value, value!, derivative, derivative!, gradient, gradient!,
                jacobian, jacobian!, hessian, hessian!

k = 4
n0, n1, n2 = rand(), rand(), rand()
x0, x1, x2 = rand(k), rand(k, k), rand(k, k, k)
s0, s1, s2 = SVector{k}(rand(k)), SMatrix{k,k}(rand(k, k)), SArray{Tuple{k,k,k}}(rand(k, k, k))
rn = DiffResult(n0, n1, n2)
rx = DiffResult(x0, x1, x2)
rs = DiffResult(s0, s1, s2)
rsmix = DiffResult(n0, s0, s1)

issimilar(x, y) = typeof(x) == typeof(y) && size(x) == size(y)
issimilar(x::DiffResult, y::DiffResult) = issimilar(value(x), value(y)) && all(issimilar, zip(x.derivs, y.derivs))
issimilar(t::Tuple) = issimilar(t...)

##############
# DiffResult #
##############

@test rn === DiffResult(n0, n1, n2)
@test rx == DiffResult(x0, x1, x2)
@test rs === DiffResult(s0, s1, s2)
@test rsmix === DiffResult(n0, s0, s1)

@test issimilar(GradientResult(x0), DiffResult(first(x0), x0))
@test issimilar(JacobianResult(x0), DiffResult(x0, similar(x0, k, k)))
@test issimilar(JacobianResult(similar(x0, k + 1), x0), DiffResult(similar(x0, k + 1), similar(x0, k + 1, k)))
@test issimilar(HessianResult(x0), DiffResult(first(x0), x0, similar(x0, k, k)))

@test GradientResult(s0) === DiffResult(first(s0), s0)
@test JacobianResult(s0) === DiffResult(s0, zeros(SMatrix{k,k,Float64}))
@test JacobianResult(SVector{k+1}(vcat(s0, 0.0)), s0) === DiffResult(SVector{k+1}(vcat(s0, 0.0)), zeros(SMatrix{k+1,k,Float64}))
@test HessianResult(s0) === DiffResult(first(s0), s0, zeros(SMatrix{k,k,Float64}))

@test eltype(rn) === typeof(n0)
@test eltype(rx) === eltype(x0)
@test eltype(rs) === eltype(s0)

rn_copy = copy(rn)
@test rn == rn_copy
@test rn === rn_copy

rx_copy = copy(rx)
@test rx == rx_copy
@test rx !== rx_copy

rs_copy = copy(rs)
@test rs == rs_copy
@test rs === rs_copy

rsmix_copy = copy(rsmix)
@test rsmix == rsmix_copy
@test rsmix === rsmix_copy

# value/value! #
#--------------#

@test value(rn) === n0
@test value(rx) === x0
@test value(rs) === s0
@test value(rsmix) === n0

rn = value!(rn, n1)
@test value(rn) === n1
rn = value!(rn, n0)

x0_new, x0_copy = rand(k), copy(x0)
rx = value!(rx, x0_new)
@test value(rx) === x0 == x0_new
rx = value!(rx, x0_copy)

s0_new = rand(k)
rs = value!(rs, s0_new)
@test value(rs) == s0_new
@test typeof(value(rs)) === typeof(s0)
rs = value!(rs, s0)

rsmix = value!(rsmix, n1)
@test value(rsmix) === n1
rsmix = value!(rsmix, n0)

rn = value!(exp, rn, n1)
@test value(rn) === exp(n1)
rn = value!(rn, n0)

x0_new, x0_copy = rand(k), copy(x0)
rx = value!(exp, rx, x0_new)
@test value(rx) === x0 == exp.(x0_new)
rx = value!(rx, x0_copy)

s0_new = rand(k)
rs = value!(exp, rs, s0_new)
@test value(rs) == exp.(s0_new)
@test typeof(value(rs)) === typeof(s0)
rs = value!(rs, s0)

rsmix = value!(exp, rsmix, n1)
@test value(rsmix) === exp(n1)
rsmix = value!(rsmix, n0)

ksqrt = Int(sqrt(k))
T = typeof(SMatrix{ksqrt,ksqrt}(rand(ksqrt,ksqrt)))
rs_new = value!(rs, convert(T, value(rs)))
@test rs_new === rs

# derivative/derivative! #
#------------------------#

@test derivative(rn) === n1
@test derivative(rn, Val{2}) === n2

@test derivative(rx) === x1
@test derivative(rx, Val{2}) === x2

@test derivative(rs) === s1
@test derivative(rs, Val{2}) === s2

@test derivative(rsmix) === s0
@test derivative(rsmix, Val{2}) === s1

rn = derivative!(rn, n0)
@test derivative(rn) === n0
rn = derivative!(rn, n1)

x1_new, x1_copy = rand(k, k), copy(x1)
rx = derivative!(rx, x1_new)
@test derivative(rx) === x1 == x1_new
rx = derivative!(rx, x1_copy)

s1_new = rand(k, k)
rs = derivative!(rs, s1_new)
@test derivative(rs) == s1_new
@test typeof(derivative(rs)) === typeof(s1)
rs = derivative!(rs, s1)

s0_new = rand(k)
rsmix = derivative!(rsmix, s0_new)
@test derivative(rsmix) == s0_new
@test typeof(derivative(rsmix)) === typeof(s0)
rsmix = derivative!(rsmix, s0)

rn = derivative!(rn, n1, Val{2})
@test derivative(rn, Val{2}) === n1
rn = derivative!(rn, n2, Val{2})

x2_new, x2_copy = rand(k, k, k), copy(x2)
rx = derivative!(rx, x2_new, Val{2})
@test derivative(rx, Val{2}) === x2 == x2_new
rx = derivative!(rx, x2_copy, Val{2})

s2_new = rand(k, k, k)
rs = derivative!(rs, s2_new, Val{2})
@test derivative(rs, Val{2}) == s2_new
@test typeof(derivative(rs, Val{2})) === typeof(s2)
rs = derivative!(rs, s2, Val{2})

s1_new = rand(k, k)
rsmix = derivative!(rsmix, s1_new, Val{2})
@test derivative(rsmix, Val{2}) == s1_new
@test typeof(derivative(rsmix, Val{2})) === typeof(s1)
rsmix = derivative!(rsmix, s1, Val{2})

rn = derivative!(exp, rn, n0)
@test derivative(rn) === exp(n0)
rn = derivative!(rn, n1)

x1_new, x1_copy = rand(k, k), copy(x1)
rx = derivative!(exp, rx, x1_new)
@test derivative(rx) === x1 == exp.(x1_new)
rx = derivative!(exp, rx, x1_copy)

s1_new = rand(k, k)
rs = derivative!(exp, rs, s1_new)
@test derivative(rs) == exp.(s1_new)
@test typeof(derivative(rs)) === typeof(s1)
rs = derivative!(exp, rs, s1)

s0_new = rand(k)
rsmix = derivative!(exp, rsmix, s0_new)
@test derivative(rsmix) == exp.(s0_new)
@test typeof(derivative(rsmix)) === typeof(s0)
rsmix = derivative!(exp, rsmix, s0)

rn = derivative!(exp, rn, n1, Val{2})
@test derivative(rn, Val{2}) === exp(n1)
rn = derivative!(rn, n2, Val{2})

x2_new, x2_copy = rand(k, k, k), copy(x2)
rx = derivative!(exp, rx, x2_new, Val{2})
@test derivative(rx, Val{2}) === x2 == exp.(x2_new)
rx = derivative!(exp, rx, x2_copy, Val{2})

s2_new = rand(k, k, k)
rs = derivative!(exp, rs, s2_new, Val{2})
@test derivative(rs, Val{2}) == exp.(s2_new)
@test typeof(derivative(rs, Val{2})) === typeof(s2)
rs = derivative!(exp, rs, s2, Val{2})

s1_new = rand(k, k)
rsmix = derivative!(exp, rsmix, s1_new, Val{2})
@test derivative(rsmix, Val{2}) == exp.(s1_new)
@test typeof(derivative(rsmix, Val{2})) === typeof(s1)
rsmix = derivative!(exp, rsmix, s1, Val{2})

# gradient/gradient! #
#--------------------#

x1_new, x1_copy = rand(k, k), copy(x1)
rx = gradient!(rx, x1_new)
@test gradient(rx) === x1 == x1_new
rx = gradient!(rx, x1_copy)

s1_new = rand(k, k)
rs = gradient!(rs, s1_new)
@test gradient(rs) == s1_new
@test typeof(gradient(rs)) === typeof(s1)
rs = gradient!(rs, s1)

x1_new, x1_copy = rand(k, k), copy(x1)
rx = gradient!(exp, rx, x1_new)
@test gradient(rx) === x1 == exp.(x1_new)
rx = gradient!(exp, rx, x1_copy)

s0_new = rand(k)
rsmix = gradient!(exp, rsmix, s0_new)
@test gradient(rsmix) == exp.(s0_new)
@test typeof(gradient(rsmix)) === typeof(s0)
rsmix = gradient!(exp, rsmix, s0)

T = typeof(SVector{k*k}(rand(k*k)))
rs_new = gradient!(rs, convert(T, gradient(rs)))
@test rs_new === rs

# jacobian/jacobian! #
#--------------------#

x1_new, x1_copy = rand(k, k), copy(x1)
rx = jacobian!(rx, x1_new)
@test jacobian(rx) === x1 == x1_new
rx = jacobian!(rx, x1_copy)

s1_new = rand(k, k)
rs = jacobian!(rs, s1_new)
@test jacobian(rs) == s1_new
@test typeof(jacobian(rs)) === typeof(s1)
rs = jacobian!(rs, s1)

x1_new, x1_copy = rand(k, k), copy(x1)
rx = jacobian!(exp, rx, x1_new)
@test jacobian(rx) === x1 == exp.(x1_new)
rx = jacobian!(exp, rx, x1_copy)

s0_new = rand(k)
rsmix = jacobian!(exp, rsmix, s0_new)
@test jacobian(rsmix) == exp.(s0_new)
@test typeof(jacobian(rsmix)) === typeof(s0)
rsmix = jacobian!(exp, rsmix, s0)

T = typeof(SVector{k*k}(rand(k*k)))
rs_new = jacobian!(rs, convert(T, jacobian(rs)))
@test rs_new === rs

# hessian/hessian! #
#------------------#

x2_new, x2_copy = rand(k, k, k), copy(x2)
rx = hessian!(rx, x2_new)
@test hessian(rx) === x2 == x2_new
rx = hessian!(rx, x2_copy)

s2_new = rand(k, k, k)
rs = hessian!(rs, s2_new)
@test hessian(rs) == s2_new
@test typeof(hessian(rs)) === typeof(s2)
rs = hessian!(rs, s2)

x2_new, x2_copy = rand(k, k, k), copy(x2)
rx = hessian!(exp, rx, x2_new)
@test hessian(rx) === x2 == exp.(x2_new)
rx = hessian!(exp, rx, x2_copy)

s1_new = rand(k, k)
rsmix = hessian!(exp, rsmix, s1_new)
@test hessian(rsmix) == exp.(s1_new)
@test typeof(hessian(rsmix)) === typeof(s1)
rsmix = hessian!(exp, rsmix, s1)

T = typeof(SVector{k*k*k}(rand(k*k*k)))
rs_new = hessian!(rs, convert(T, hessian(rs)))
@test rs_new === rs

end # module
